LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 150

Search options

  1. Article ; Online: Introduction to the special issue: Prostate Cancer Update.

    Westphalen, Antonio C

    Abdominal radiology (New York)

    2020  Volume 45, Issue 12, Page(s) 3947

    MeSH term(s) Humans ; Male ; Prostatic Neoplasms/diagnostic imaging
    Language English
    Publishing date 2020-11-21
    Publishing country United States
    Document type Editorial
    ZDB-ID 2839786-1
    ISSN 2366-0058 ; 2366-004X
    ISSN (online) 2366-0058
    ISSN 2366-004X
    DOI 10.1007/s00261-020-02861-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  2. Article ; Online: Lost in translation: lessons learned from the "demise" of MRSI of the prostate.

    Westphalen, Antonio C

    Abdominal radiology (New York)

    2019  Volume 44, Issue 9, Page(s) 3185–3187

    Abstract: At times, technologies fail for reasons other than an inability to deliver on their promises. The iconic Blackberry, for example, was once coined "Research in Motion", sold tens of millions of units, and then "disappeared" from the market because it did ... ...

    Abstract At times, technologies fail for reasons other than an inability to deliver on their promises. The iconic Blackberry, for example, was once coined "Research in Motion", sold tens of millions of units, and then "disappeared" from the market because it did not accompany the new trends in design. Promising technologies may also "disappear" in the medical field. What follows is the tale of the rise and fall of proton magnetic resonance spectroscopic imaging (
    MeSH term(s) Humans ; Magnetic Resonance Spectroscopy/methods ; Male ; Prostate/diagnostic imaging ; Prostatic Neoplasms/diagnostic imaging ; Protons
    Chemical Substances Protons
    Language English
    Publishing date 2019-06-27
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2839786-1
    ISSN 2366-0058 ; 2366-004X
    ISSN (online) 2366-0058
    ISSN 2366-004X
    DOI 10.1007/s00261-019-02114-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  3. Article ; Online: The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging.

    Canellas, Rodrigo / Kohli, Marc D / Westphalen, Antonio C

    Current oncology reports

    2023  Volume 25, Issue 4, Page(s) 243–250

    Abstract: Purpose of review: The purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging.: Recent findings: Artificial intelligence has been applied to prostate cancer MR imaging to improve its ...

    Abstract Purpose of review: The purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging.
    Recent findings: Artificial intelligence has been applied to prostate cancer MR imaging to improve its diagnostic accuracy and reproducibility of interpretation. Multiple models have been tested for gland segmentation and volume calculation, automated lesion detection, localization, and characterization, as well as prediction of tumor aggressiveness and tumor recurrence. Studies show, for example, that very robust automated gland segmentation and volume calculations can be achieved and that lesions can be detected and accurately characterized. Although results are promising, we should view these with caution. Most studies included a small sample of patients from a single institution and most models did not undergo proper external validation. More research is needed with larger and well-design studies for the development of reliable artificial intelligence tools.
    MeSH term(s) Male ; Humans ; Artificial Intelligence ; Reproducibility of Results ; Neoplasm Recurrence, Local ; Magnetic Resonance Imaging/methods ; Prostatic Neoplasms/pathology
    Language English
    Publishing date 2023-02-07
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2057359-5
    ISSN 1534-6269 ; 1523-3790
    ISSN (online) 1534-6269
    ISSN 1523-3790
    DOI 10.1007/s11912-023-01371-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  4. Article ; Online: SAR Prostate Cancer Disease-Focused Panel report.

    Chang, Silvia D / Westphalen, Antonio C

    Abdominal radiology (New York)

    2020  Volume 45, Issue 12, Page(s) 3948–3950

    MeSH term(s) Humans ; Male ; Prostate-Specific Antigen ; Prostatic Diseases ; Prostatic Neoplasms/diagnostic imaging
    Chemical Substances Prostate-Specific Antigen (EC 3.4.21.77)
    Language English
    Publishing date 2020-11-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2839786-1
    ISSN 2366-0058 ; 2366-004X
    ISSN (online) 2366-0058
    ISSN 2366-004X
    DOI 10.1007/s00261-020-02862-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  5. Article: Overcoming the challenges of imaging patients with metabolic syndrome.

    Ohliger, Michael A / Westphalen, Antonio C

    Radiologia brasileira

    2019  Volume 52, Issue 1, Page(s) V–VI

    Language English
    Publishing date 2019-02-26
    Publishing country Brazil
    Document type Editorial
    ZDB-ID 2078806-X
    ISSN 1678-7099 ; 0100-3984
    ISSN (online) 1678-7099
    ISSN 0100-3984
    DOI 10.1590/0100-3984.2019.52.1e1
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  6. Article ; Online: Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI.

    Rajagopal, Abhejit / Westphalen, Antonio C / Velarde, Nathan / Simko, Jeffry P / Nguyen, Hao / Hope, Thomas A / Larson, Peder E Z / Magudia, Kirti

    IEEE transactions on medical imaging

    2024  Volume PP

    Abstract: Non-invasive prostate cancer classification from MRI has the potential to revolutionize patient care by providing early detection of clinically significant disease, but has thus far shown limited positive predictive value. To address this, we present a ... ...

    Abstract Non-invasive prostate cancer classification from MRI has the potential to revolutionize patient care by providing early detection of clinically significant disease, but has thus far shown limited positive predictive value. To address this, we present a image-based deep learning method to predict clinically significant prostate cancer from screening MRI in patients that subsequently underwent biopsy with results ranging from benign pathology to the highest grade tumors. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. Where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n = 198) multi-parametric prostate MRI exams collected at UCSF from 2016-2019 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can feasibly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation (71.6% vs 66.7% balanced accuracy and 0.724 vs 0.716 AUC).
    Language English
    Publishing date 2024-03-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2024.3382909
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  7. Article: Multiparametric magnetic resonance imaging of the prostate-a basic tutorial.

    Cabarrus, Miguel C / Westphalen, Antonio C

    Translational andrology and urology

    2017  Volume 6, Issue 3, Page(s) 376–386

    Abstract: Prostate cancer is the second most common cause of cancer related death in the United States and the most commonly diagnosed malignancy in men. In general, prostate cancer is slow growing, though there is a broad spectrum of disease that may be indolent, ...

    Abstract Prostate cancer is the second most common cause of cancer related death in the United States and the most commonly diagnosed malignancy in men. In general, prostate cancer is slow growing, though there is a broad spectrum of disease that may be indolent, or aggressive and rapidly progressive. Screening for prostate is controversial and complicated by lack of specificity and over diagnosis of clinically insignificant cancer. Imaging has played a role in diagnosis of prostate cancer, primarily through systemic transrectal ultrasound (TRUS) guided biopsy. While TRUS guided biopsy radically changed prostate cancer diagnosis, it still remains limited by low resolution, poor tissue characterization, relatively low sensitivity and positive predictive value. Advances in multiparametric magnetic resonance imaging (mpMRI) have allowed more accurate detection, localization, and staging as well as aiding in the role of active surveillance (AS). The use of mpMRI for the evaluation of prostate cancer has increased dramatically and this trend is likely to continue as the technique is rapidly improving and its applications expand. The purpose of this article is to review the basic principles of mpMRI of the prostate and its clinical applications, which will be reviewed in greater detail in subsequent chapters of this issue.
    Language English
    Publishing date 2017-06-30
    Publishing country China
    Document type Journal Article ; Review
    ZDB-ID 2851630-8
    ISSN 2223-4691 ; 2223-4691 ; 2223-4683
    ISSN (online) 2223-4691
    ISSN 2223-4691 ; 2223-4683
    DOI 10.21037/tau.2017.01.06
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  8. Article ; Online: Improving the Safety of Computed Tomography Through Automated Quality Measurement: A Radiologist Reader Study of Radiation Dose, Image Noise, and Image Quality.

    Smith-Bindman, Rebecca / Wang, Yifei / Stewart, Carly / Luong, Jason / Chu, Philip W / Kohli, Marc / Westphalen, Antonio C / Siegel, Eliot / Ray, Monika / Szczykutowicz, Timothy P / Bindman, Andrew B / Romano, Patrick S

    Investigative radiology

    2024  

    Abstract: Objectives: The Centers for Medicare and Medicaid Services funded the development of a computed tomography (CT) quality measure for use in pay-for-performance programs, which balances automated assessments of radiation dose with image quality to ... ...

    Abstract Objectives: The Centers for Medicare and Medicaid Services funded the development of a computed tomography (CT) quality measure for use in pay-for-performance programs, which balances automated assessments of radiation dose with image quality to incentivize dose reduction without compromising the diagnostic utility of the tests. However, no existing quantitative method for assessing CT image quality has been validated against radiologists' image quality assessments on a large number of CT examinations. Thus to develop an automated measure of image quality, we tested the relationship between radiologists' subjective ratings of image quality with measurements of radiation dose and image noise.
    Materials and methods: Board-certified, posttraining, clinically active radiologists rated the image quality of 200 diagnostic CT examinations from a set of 734, representing 14 CT categories. Examinations with significant distractions, motion, or artifact were excluded. Radiologists rated diagnostic image quality as excellent, adequate, marginally acceptable, or poor; the latter 2 were considered unacceptable for rendering diagnoses. We quantified the relationship between ratings and image noise and radiation dose, by category, by analyzing the odds of an acceptable rating per standard deviation (SD) increase in noise or geometric SD (gSD) in dose.
    Results: One hundred twenty-five radiologists contributed 24,800 ratings. Most (89%) were acceptable. The odds of an examination being rated acceptable statistically significantly increased per gSD increase in dose and decreased per SD increase in noise for most categories, including routine dose head, chest, and abdomen-pelvis, which together comprise 60% of examinations performed in routine practice. For routine dose abdomen-pelvis, the most common category, each gSD increase in dose raised the odds of an acceptable rating (2.33; 95% confidence interval, 1.98-3.24), whereas each SD increase in noise decreased the odds (0.90; 0.79-0.99). For only 2 CT categories, high-dose head and neck/cervical spine, neither dose nor noise was associated with ratings.
    Conclusions: Radiation dose and image noise correlate with radiologists' image quality assessments for most CT categories, making them suitable as automated metrics in quality programs incentivizing reduction of excessive radiation doses.
    Language English
    Publishing date 2024-01-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80345-5
    ISSN 1536-0210 ; 0020-9996
    ISSN (online) 1536-0210
    ISSN 0020-9996
    DOI 10.1097/RLI.0000000000001062
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  9. Article ; Online: Diagnosis of renal angiomyolipoma with CT hounsfield unit thresholds.

    Westphalen, Antonio C

    Radiology

    2012  Volume 262, Issue 1, Page(s) 370–1; author reply 371–2

    MeSH term(s) Algorithms ; Angiomyolipoma/diagnostic imaging ; Female ; Humans ; Kidney Neoplasms/diagnostic imaging ; Male ; Radiographic Image Enhancement/methods ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2012-01
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.11111592
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  10. Article ; Online: Gastrointestinal Stromal Tumor Incidentally Detected on 18F-Fluciclovine PET/CT.

    Raghavan, Kesav / Flavell, Robert R / Westphalen, Antonio C / Behr, Spencer C

    Clinical nuclear medicine

    2020  Volume 46, Issue 4, Page(s) 345–347

    Abstract: Abstract: We present a case of metastatic gastrointestinal stromal tumor incidentally detected on 18F-fluciclovine PET/CT. A 68-year-old man with history of intermediate-risk prostate cancer (Gleason score 4 + 3 = 7; pT2cN0M0) previously treated with ... ...

    Abstract Abstract: We present a case of metastatic gastrointestinal stromal tumor incidentally detected on 18F-fluciclovine PET/CT. A 68-year-old man with history of intermediate-risk prostate cancer (Gleason score 4 + 3 = 7; pT2cN0M0) previously treated with retropubic radical prostatectomy, adjuvant whole pelvis radiation, and androgen deprivation therapy (leuprolide) presented with slowly rising serum prostate-specific antigen over 3 years, concerning for recurrent prostate cancer. To identify potential sites of recurrent disease, an 18F-fluciclovine PET/CT was obtained. Multiple tracer-avid mesenteric masses and enlarged lymph nodes were found throughout the abdomen and pelvis, later biopsy-proven to reflect metastatic gastrointestinal stromal tumor.
    MeSH term(s) Aged ; Carboxylic Acids ; Cyclobutanes ; Gastrointestinal Stromal Tumors/diagnostic imaging ; Gastrointestinal Stromal Tumors/pathology ; Gastrointestinal Stromal Tumors/secondary ; Humans ; Incidental Findings ; Male ; Neoplasm Grading ; Positron Emission Tomography Computed Tomography ; Prostatic Neoplasms/pathology
    Chemical Substances Carboxylic Acids ; Cyclobutanes ; fluciclovine F-18 (38R1Q0L1ZE)
    Language English
    Publishing date 2020-11-12
    Publishing country United States
    Document type Case Reports ; Journal Article
    ZDB-ID 197628-x
    ISSN 1536-0229 ; 0363-9762
    ISSN (online) 1536-0229
    ISSN 0363-9762
    DOI 10.1097/RLU.0000000000003426
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

To top